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Model: openmed-community/AFM-4.5B-OpenMed-RL-CoT Source: Original Platform
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README.md
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---
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base_model: arcee-ai/AFM-4.5B
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- medical
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- instruction-tuned
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- dpo
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- grpo
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- cot
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- mergekit
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- arcee-fusion
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- openmed
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license: apache-2.0
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---
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# AFM-4.5B-OpenMed-RL-CoT
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**Lightweight medical finetune on top of Arcee’s AFM-4.5B** for education and research use. Trained using a straightforward 3-step process (SFT → DPO → GRPO-CoT) for optimal CoT enrichment.
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More information about our **methodology** will be available in a forthcoming **blog post**.
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All experiments were performed on **AMD MI300x** GPUs, with computing credits generously provided by [Hot AISLE](https://hotaisle.xyz/).
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> ⚠️ **Medical safety**
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> This model is **not** a clinician. It can hallucinate and should **not** be used for diagnosis or treatment. Always involve qualified medical professionals.
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---
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## TL;DR
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- **Base:** [`arcee-ai/AFM-4.5B`](https://huggingface.co/arcee-ai/AFM-4.5B) – Arcee’s 4.5B instruction model intended for cloud-to-edge deployment.
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- **Training (high level):**
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1) **SFT** proprietary synthetic medical datasets + **tool-calling (search) traces**
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2) **DPO** using **MedMCQA-derived** preferences (multiple-choice signal)
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3) **GRPO** for **chain-of-thought enrichment**, using **MedReason** verifiable rewards; short rationales encouraged, final answer checked.
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- **Eval (EleutherAI harness; author’s settings, bs=64)**
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- **MMLU:** **61.40** (vs **55.53** base)
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- **MMLU-Pro:** **33.16** (vs **32.61** base) – harder 10-choice variant.
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- **IFEVAL:** **59.59** (vs **63.67** base) – verifiable instruction following.
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_Note:_ Arcee’s internal evals may use different harnesses; avoid cross-harness comparisons.
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---
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## What’s inside
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### Specialization steps
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1. **Domain SFT (medical + tools)**
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Instruction-style synthetic medical Q&A + conversions; supervised **search/tool-use traces** to teach function-calling patterns compatible with chat templates.
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2. **Preference alignment — DPO**
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Uses **MedMCQA** correctness as a proxy preference signal to bias toward concise, clinically reasonable options.
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3. **Reasoning enrichment — GRPO (CoT)**
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**Group Relative Policy Optimization** without a critic; groups of sampled solutions are scored by **verifiable rewards** (answer correctness + light format checks). Trained with **MedReason** QA signal.
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---
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## Intended use & limitations
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**Intended:** Medical SLM's **research**, tool-augmented retrieval demos.
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**Out of scope:** Unsupervised patient care, generating prescriptions, and time-critical guideline decisions.
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---
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## Evaluation
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> Author-run with the EleutherAI `lm-evaluation-harness`; seeds, prompts, and templates affect absolute scores.
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| Benchmark | AFM-4.5B-OpenMed-RL-CoT | AFM-4.5B (same harness) |
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|---|---:|---:|
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| **MMLU** | **61.40** | 55.53 |
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| **MMLU-Pro** | **33.16** | 32.61 |
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| **IFEVAL** | 59.59 | **63.67** |
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- **MMLU-Pro** increases difficulty (10 options; more reasoning-heavy); small deltas are still meaningful.
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- **IFEVAL** checks **verifiable** constraints (length, keyword counts, format, etc.).
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| mmlu | AFM-4.5B-OpenMed-RL-CoT | AFM-4.5B |
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| :-------------------- | :---------------------- | :------- |
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| **other** | | |
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| clinical_knowledge | 69.43 | 65.66 |
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| college_medicine | 63.58 | 54.34 |
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| professional_medicine | 62.87 | 59.56 |
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| virology | 49.40 | 48.19 |
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| **stem** | | |
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| anatomy | 62.96 | 56.30 |
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| college_biology | 78.47 | 65.97 |
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| college_chemistry | 42.00 | 37.00 |
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| high_school_biology | 79.68 | 71.29 |
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| high_school_chemistry | 53.69 | 43.84 |
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| **groups** | | |
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| humanities | 56.20 | 50.46 |
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| other | 69.10 | 63.47 |
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| social sciences | 74.13 | 68.61 |
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| stem | 49.16 | 42.53 |
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### Reproduce (example commands)
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```bash
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# MMLU classic
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lm_eval --model hf \
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--model_args pretrained=openmed-community/AFM-4.5B-OpenMed-RL-CoT,parallelize=True,dtype=bfloat16,trust_remote_code=True \
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--task mmlu \
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--batch_size=64 \
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--apply_chat_template \
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--output_path=results \
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--fewshot_as_multiturn
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# MMLU-Pro (10-choice)
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lm_eval --model hf \
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--model_args pretrained=openmed-community/AFM-4.5B-OpenMed-RL-CoT,parallelize=True,dtype=bfloat16,trust_remote_code=True \
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--tasks leaderboard_mmlu_pro \
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--batch_size=64 \
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--apply_chat_template \
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--output_path=results \
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--fewshot_as_multiturn
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# IFEVAL (verifiable instruction following)
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lm_eval --model hf \
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--model_args pretrained=openmed-community/AFM-4.5B-OpenMed-RL-CoT,parallelize=True,dtype=bfloat16,trust_remote_code=True \
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--tasks leaderboard_ifeval \
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--batch_size=64 \
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--apply_chat_template \
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--output_path=results \
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--fewshot_as_multiturn
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```
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---
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## Quickstart (Transformers)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "openmed-community/AFM-4.5B-OpenMed-RL-CoT"
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tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
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messages = [
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{"role": "system", "content": "You are a careful medical assistant. Cite sources and warn this is not medical advice. Think step-by-step."},
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{"role": "user", "content": "Briefly: cellulitis vs erysipelas differences?"}
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]
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prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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inputs = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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## Data & training notes
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* **SFT data:** Proprietary synthetic medical data + search traces.
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* **DPO signal:** Preferences derived from **MedMCQA** multiple-choice correctness.
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* **GRPO reward:** Answer-checking + format verifiers; **MedReason** used to shape faithful, short CoT.
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* No known PHI; please open an issue if you spot any.
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---
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## Compatibility & licenses
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* **Base model:** AFM-4.5B (Arcee). Refer to the base card/blog for architecture and usage details. License for AFM releases is **Apache 2.0**;
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---
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## Additional note
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We also provide a **merged** [openmed-community/AFM-4.5B-OpenMed](https://huggingface.co/openmed-community/AFM-4.5B-OpenMed) version after step 3 (**GRPO**). In our harness, it shows **worse CoT** behavior but a significant gain on **IFEVAL**.
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config.json
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{
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"architectures": [
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"ArceeForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": 128003,
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"head_dim": 128,
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"hidden_act": "relu2",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 18432,
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"max_position_embeddings": 65536,
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"mlp_bias": false,
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"model_type": "arcee",
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"num_attention_heads": 20,
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"num_hidden_layers": 36,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"beta_fast": 32.0,
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"beta_slow": 1.0,
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"factor": 20.0,
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"mscale": 1.0,
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"original_max_position_embeddings": 4096,
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"rope_type": "yarn",
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"type": "yarn"
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},
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"use_cache": false,
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"vocab_size": 128005
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}
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d48708c6021027e8fc6d5342e1498111d8e87aae8903319d3ead1fbdfc4a9125
|
||||
size 17158115
|
||||
56
tokenizer_config.json
Normal file
56
tokenizer_config.json
Normal file
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"128000": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"128001": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"128002": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"128003": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"128004": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|begin_of_text|>",
|
||||
"chat_template": "{%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n{%- else %}\n {{- '<|im_start|>system\\nThe assistant is AFM-4.5B, trained by Arcee AI, with 4.5 billion parameters. AFM is a deeply thoughtful, helpful assistant. The assistant is having a conversation with the user. The assistant\\'s responses are calm, intelligent, and personable, always aiming to truly understand the user\\'s intent. AFM thinks aloud, step by step, when solving problems or forming explanations, much like a careful, reflective thinker would. The assistant helps with sincerity and depth. If a topic invites introspection, curiosity, or broader insight, the assistant allows space for reflection — be open to nuance and complexity. The assistant is not robotic or overly formal; it speaks like a wise, thoughtful companion who cares about clarity and the human experience. If a topic is uncertain or depends on subjective interpretation, AFM explains the possibilities thoughtfully.<|im_end|>\\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if not (message.role == 'system' and loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if messages[-1]['role'] != 'assistant' %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_input_names": [
|
||||
"input_ids",
|
||||
"attention_mask"
|
||||
],
|
||||
"model_max_length": 65536,
|
||||
"pad_token": "<|finetune_right_pad_id|>",
|
||||
"tokenizer_class": "PreTrainedTokenizerFast"
|
||||
}
|
||||
Reference in New Issue
Block a user